A government commission is currently considering an innovation that could be as transformational for Artificial Intelligence (AI) as a hadron collider is for physics. It's called a National Research Cloud, and right now the federal National AI Research Resource Task Force (NAIRR) is determining how we can develop such a cloud to broaden access to computing and data and spur basic and non-commercial AI research. At stake may be rates of investment in basic scientific research not seen since the days of the Cold War.

Personalisation; everybody knows they should be doing it, but not everyone knows exactly how best to achieve it. The ubiquity of platforms like Facebook and Amazon has lead to a seismic shift in user expectations when it comes to personalisation. A recent study by Monetate found that more than 83% of customers reported they expect brands to personalise experiences for them. In combination with conversational user interfaces, AI presents brands with the opportunity to engage in a meaningful dialogue with their audiences. Many brands however, have struggled to deliver real-time personalisation at scale.

We all know that Google, Facebook and Amazon, are investing heavily in AI, with results widely reported. It's no surprise that businesses built around collecting and analysing data are leading the way in AI. Businesses built in the pre-digital age, based on physical products and infrastructure. Business that underpin transport and energy, or that develop new medicines or materials. How should these businesses take advantage of AI?

Ashley Madison users have already unwittingly encountered a great deal of bots, but in 2017 they are going to become more prevalent – and more transparent to the user, according to U.K.-based Juniper Research. In its top 10 predictions for the year, the organization forecasts that chatbots – virtual agents which operate via social networks and messaging platforms – will grow in presence and popularity, streamlining e-commerce activities such as booking flights and hotels, or to order items directly by speaking with a bot through an app. The research firm also expects there will be more opportunities for businesses to personalize these services, using social media analytics to provide consumers with specially tailored incentives. While acknowledging that many messaging bots have not seen a significant uptake in Western markets yet, due to their basic features and the technology not being fully-developed, Juniper noted that in Asian markets including China platforms such as WeChat have already established chatbots that aid in daily tasks. Meanwhile, North American applications such as Facebook Messenger are finally beginning to drive chatbot integration using ads. Bots won't be the only helpful technology to get more widespread use this year: With Google Assistant coming to multiple devices and Alexa, Cortana and Siri already available on several platforms, Juniper said 2017 will be the year when digital assistants become sufficiently mainstream for the device ecosystem to become a key battleground.

At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. In this post we present predictions from those in industry, which do not follow a prescribed question but which do address what to keep a look out for in different sectors in the upcoming year. Quotes are organized alphabetically by name of the company which they have been submitted on behalf of, and we have reserved the right to edit (extract excerpts from) for both content and length. As the predictions come from across industry and reach into its many different niche sectors, there is no general consensus or over-arching themes herein, which makes intuitive sense.

The world's biggest technology companies are handing over the keys to their success, making their artificial intelligence systems open-source. Traditionally, computer users could see the end product of what a piece of software did by, for instance, writing a document in Microsoft Word or playing a video game. But the underlying programming -- the source code -- was proprietary, kept from public view. Opening source material in computer science is a big deal because the more people that look at code, the more likely it is that bugs and long-term opportunities and risks can be worked out. Openness is increasingly a big deal in science as well, for similar reasons.